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8e8e4bbabc
opencv/modules/dnn/test/test_ie_models.cpp
luz paz 8e8e4bbabc dnn: fix various dnn related typos 
Fixes source comments and documentation related to dnn code.
2022-03-23 18:12:12 -04:00

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2018-2019, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "test_precomp.hpp"
#ifdef HAVE_INF_ENGINE
#include <opencv2/core/utils/filesystem.hpp>
//
// Synchronize headers include statements with src/op_inf_engine.hpp
//
//#define INFERENCE_ENGINE_DEPRECATED // turn off deprecation warnings from IE
//there is no way to suppress warnings from IE only at this moment, so we are forced to suppress warnings globally
#if defined(__GNUC__)
#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
#endif
#ifdef _MSC_VER
#pragma warning(disable: 4996) // was declared deprecated
#endif
#if defined(__GNUC__)
#pragma GCC visibility push(default)
#endif
#include <inference_engine.hpp>
#include <ie_icnn_network.hpp>
#include <ie_extension.h>
#if defined(__GNUC__)
#pragma GCC visibility pop
#endif
namespace opencv_test { namespace {
static void initDLDTDataPath()
{
#ifndef WINRT
static bool initialized = false;
if (!initialized)
{
#if INF_ENGINE_RELEASE <= 2018050000
const char* dldtTestDataPath = getenv("INTEL_CVSDK_DIR");
if (dldtTestDataPath)
cvtest::addDataSearchPath(dldtTestDataPath);
#else
const char* omzDataPath = getenv("OPENCV_OPEN_MODEL_ZOO_DATA_PATH");
if (omzDataPath)
cvtest::addDataSearchPath(omzDataPath);
const char* dnnDataPath = getenv("OPENCV_DNN_TEST_DATA_PATH");
if (dnnDataPath)
cvtest::addDataSearchPath(std::string(dnnDataPath) + "/omz_intel_models");
#endif
initialized = true;
}
#endif
}
using namespace cv;
using namespace cv::dnn;
using namespace InferenceEngine;
struct OpenVINOModelTestCaseInfo
{
const char* modelPathFP32;
const char* modelPathFP16;
};
static const std::map<std::string, OpenVINOModelTestCaseInfo>& getOpenVINOTestModels()
{
static std::map<std::string, OpenVINOModelTestCaseInfo> g_models {
#if INF_ENGINE_RELEASE >= 2018050000 && \
INF_ENGINE_RELEASE <= 2020999999 // don't use IRv5 models with 2020.1+
// layout is defined by open_model_zoo/model_downloader
// Downloaded using these parameters for Open Model Zoo downloader (2019R1):
// ./downloader.py -o ${OPENCV_DNN_TEST_DATA_PATH}/omz_intel_models --cache_dir ${OPENCV_DNN_TEST_DATA_PATH}/.omz_cache/ \
// --name face-person-detection-retail-0002,face-person-detection-retail-0002-fp16,age-gender-recognition-retail-0013,age-gender-recognition-retail-0013-fp16,head-pose-estimation-adas-0001,head-pose-estimation-adas-0001-fp16,person-detection-retail-0002,person-detection-retail-0002-fp16,vehicle-detection-adas-0002,vehicle-detection-adas-0002-fp16
{ "age-gender-recognition-retail-0013", {
"Retail/object_attributes/age_gender/dldt/age-gender-recognition-retail-0013",
"Retail/object_attributes/age_gender/dldt/age-gender-recognition-retail-0013-fp16"
}},
{ "face-person-detection-retail-0002", {
"Retail/object_detection/face_pedestrian/rmnet-ssssd-2heads/0002/dldt/face-person-detection-retail-0002",
"Retail/object_detection/face_pedestrian/rmnet-ssssd-2heads/0002/dldt/face-person-detection-retail-0002-fp16"
}},
{ "head-pose-estimation-adas-0001", {
"Transportation/object_attributes/headpose/vanilla_cnn/dldt/head-pose-estimation-adas-0001",
"Transportation/object_attributes/headpose/vanilla_cnn/dldt/head-pose-estimation-adas-0001-fp16"
}},
{ "person-detection-retail-0002", {
"Retail/object_detection/pedestrian/hypernet-rfcn/0026/dldt/person-detection-retail-0002",
"Retail/object_detection/pedestrian/hypernet-rfcn/0026/dldt/person-detection-retail-0002-fp16"
}},
{ "vehicle-detection-adas-0002", {
"Transportation/object_detection/vehicle/mobilenet-reduced-ssd/dldt/vehicle-detection-adas-0002",
"Transportation/object_detection/vehicle/mobilenet-reduced-ssd/dldt/vehicle-detection-adas-0002-fp16"
}},
#endif
#if INF_ENGINE_RELEASE >= 2020010000
// Downloaded using these parameters for Open Model Zoo downloader (2020.1):
// ./downloader.py -o ${OPENCV_DNN_TEST_DATA_PATH}/omz_intel_models --cache_dir ${OPENCV_DNN_TEST_DATA_PATH}/.omz_cache/ \
// --name person-detection-retail-0013,age-gender-recognition-retail-0013
{ "person-detection-retail-0013", { // IRv10
"intel/person-detection-retail-0013/FP32/person-detection-retail-0013",
"intel/person-detection-retail-0013/FP16/person-detection-retail-0013"
}},
{ "age-gender-recognition-retail-0013", {
"intel/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013",
"intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013"
}},
#endif
#if INF_ENGINE_RELEASE >= 2021020000
// OMZ: 2020.2
{ "face-detection-0105", {
"intel/face-detection-0105/FP32/face-detection-0105",
"intel/face-detection-0105/FP16/face-detection-0105"
}},
{ "face-detection-0106", {
"intel/face-detection-0106/FP32/face-detection-0106",
"intel/face-detection-0106/FP16/face-detection-0106"
}},
#endif
#if INF_ENGINE_RELEASE >= 2021040000
// OMZ: 2021.4
{ "person-vehicle-bike-detection-2004", {
"intel/person-vehicle-bike-detection-2004/FP32/person-vehicle-bike-detection-2004",
"intel/person-vehicle-bike-detection-2004/FP16/person-vehicle-bike-detection-2004"
//"intel/person-vehicle-bike-detection-2004/FP16-INT8/person-vehicle-bike-detection-2004"
}},
#endif
};
return g_models;
}
static const std::vector<std::string> getOpenVINOTestModelsList()
{
std::vector<std::string> result;
const std::map<std::string, OpenVINOModelTestCaseInfo>& models = getOpenVINOTestModels();
for (const auto& it : models)
result.push_back(it.first);
return result;
}
inline static std::string getOpenVINOModel(const std::string &modelName, bool isFP16)
{
const std::map<std::string, OpenVINOModelTestCaseInfo>& models = getOpenVINOTestModels();
const auto it = models.find(modelName);
if (it != models.end())
{
OpenVINOModelTestCaseInfo modelInfo = it->second;
if (isFP16 && modelInfo.modelPathFP16)
return std::string(modelInfo.modelPathFP16);
else if (!isFP16 && modelInfo.modelPathFP32)
return std::string(modelInfo.modelPathFP32);
}
return std::string();
}
static inline void genData(const InferenceEngine::TensorDesc& desc, Mat& m, Blob::Ptr& dataPtr)
{
const std::vector<size_t>& dims = desc.getDims();
if (desc.getPrecision() == InferenceEngine::Precision::FP32)
{
m.create(std::vector<int>(dims.begin(), dims.end()), CV_32F);
randu(m, -1, 1);
dataPtr = make_shared_blob<float>(desc, (float*)m.data);
}
else if (desc.getPrecision() == InferenceEngine::Precision::I32)
{
m.create(std::vector<int>(dims.begin(), dims.end()), CV_32S);
randu(m, -100, 100);
dataPtr = make_shared_blob<int>(desc, (int*)m.data);
}
else
{
FAIL() << "Unsupported precision: " << desc.getPrecision();
}
}
void runIE(Target target, const std::string& xmlPath, const std::string& binPath,
std::map<std::string, cv::Mat>& inputsMap, std::map<std::string, cv::Mat>& outputsMap)
{
SCOPED_TRACE("runIE");
std::string device_name;
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019010000)
Core ie;
#else
InferenceEnginePluginPtr enginePtr;
InferencePlugin plugin;
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019030000)
CNNNetwork net = ie.ReadNetwork(xmlPath, binPath);
#else
CNNNetReader reader;
reader.ReadNetwork(xmlPath);
reader.ReadWeights(binPath);
CNNNetwork net = reader.getNetwork();
#endif
ExecutableNetwork netExec;
InferRequest infRequest;
try
{
switch (target)
{
case DNN_TARGET_CPU:
device_name = "CPU";
break;
case DNN_TARGET_OPENCL:
case DNN_TARGET_OPENCL_FP16:
device_name = "GPU";
break;
case DNN_TARGET_MYRIAD:
device_name = "MYRIAD";
break;
case DNN_TARGET_FPGA:
device_name = "FPGA";
break;
default:
CV_Error(Error::StsNotImplemented, "Unknown target");
};
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
auto dispatcher = InferenceEngine::PluginDispatcher({""});
enginePtr = dispatcher.getPluginByDevice(device_name);
#endif
if (target == DNN_TARGET_CPU || target == DNN_TARGET_FPGA)
{
std::string suffixes[] = {"_avx2", "_sse4", ""};
bool haveFeature[] = {
checkHardwareSupport(CPU_AVX2),
checkHardwareSupport(CPU_SSE4_2),
true
};
for (int i = 0; i < 3; ++i)
{
if (!haveFeature[i])
continue;
#ifdef _WIN32
std::string libName = "cpu_extension" + suffixes[i] + ".dll";
#elif defined(__APPLE__)
std::string libName = "libcpu_extension" + suffixes[i] + ".dylib";
#else
std::string libName = "libcpu_extension" + suffixes[i] + ".so";
#endif // _WIN32
try
{
IExtensionPtr extension = make_so_pointer<IExtension>(libName);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019010000)
ie.AddExtension(extension, device_name);
#else
enginePtr->AddExtension(extension, 0);
#endif
break;
}
catch(...) {}
}
// Some of networks can work without a library of extra layers.
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019010000)
netExec = ie.LoadNetwork(net, device_name);
#else
plugin = InferencePlugin(enginePtr);
netExec = plugin.LoadNetwork(net, {});
#endif
infRequest = netExec.CreateInferRequest();
}
catch (const std::exception& ex)
{
CV_Error(Error::StsAssert, format("Failed to initialize Inference Engine backend: %s", ex.what()));
}
// Fill input blobs.
inputsMap.clear();
BlobMap inputBlobs;
for (auto& it : net.getInputsInfo())
{
const InferenceEngine::TensorDesc& desc = it.second->getTensorDesc();
genData(desc, inputsMap[it.first], inputBlobs[it.first]);
if (cvtest::debugLevel > 0)
{
const std::vector<size_t>& dims = desc.getDims();
std::cout << "Input: '" << it.first << "' precision=" << desc.getPrecision() << " dims=" << dims.size() << " [";
for (auto d : dims)
std::cout << " " << d;
std::cout << "] ocv_mat=" << inputsMap[it.first].size << " of " << typeToString(inputsMap[it.first].type()) << std::endl;
}
}
infRequest.SetInput(inputBlobs);
// Fill output blobs.
outputsMap.clear();
BlobMap outputBlobs;
for (auto& it : net.getOutputsInfo())
{
const InferenceEngine::TensorDesc& desc = it.second->getTensorDesc();
genData(desc, outputsMap[it.first], outputBlobs[it.first]);
if (cvtest::debugLevel > 0)
{
const std::vector<size_t>& dims = desc.getDims();
std::cout << "Output: '" << it.first << "' precision=" << desc.getPrecision() << " dims=" << dims.size() << " [";
for (auto d : dims)
std::cout << " " << d;
std::cout << "] ocv_mat=" << outputsMap[it.first].size << " of " << typeToString(outputsMap[it.first].type()) << std::endl;
}
}
infRequest.SetOutput(outputBlobs);
infRequest.Infer();
}
void runCV(Backend backendId, Target targetId, const std::string& xmlPath, const std::string& binPath,
const std::map<std::string, cv::Mat>& inputsMap,
std::map<std::string, cv::Mat>& outputsMap)
{
SCOPED_TRACE("runOCV");
Net net = readNet(xmlPath, binPath);
for (auto& it : inputsMap)
net.setInput(it.second, it.first);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
std::vector<String> outNames = net.getUnconnectedOutLayersNames();
if (cvtest::debugLevel > 0)
{
std::cout << "OpenCV output names: " << outNames.size() << std::endl;
for (auto name : outNames)
std::cout << "- " << name << std::endl;
}
std::vector<Mat> outs;
net.forward(outs, outNames);
outputsMap.clear();
EXPECT_EQ(outs.size(), outNames.size());
for (int i = 0; i < outs.size(); ++i)
{
EXPECT_TRUE(outputsMap.insert({outNames[i], outs[i]}).second);
}
}
typedef TestWithParam<tuple< tuple<Backend, Target>, std::string> > DNNTestOpenVINO;
TEST_P(DNNTestOpenVINO, models)
{
initDLDTDataPath();
const Backend backendId = get<0>(get<0>(GetParam()));
const Target targetId = get<1>(get<0>(GetParam()));
std::string modelName = get<1>(GetParam());
ASSERT_FALSE(backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) <<
"Inference Engine backend is required";
#if INF_ENGINE_VER_MAJOR_GE(2021030000)
if (targetId == DNN_TARGET_MYRIAD && (false
|| modelName == "person-detection-retail-0013" // ncDeviceOpen:1013 Failed to find booted device after boot
|| modelName == "age-gender-recognition-retail-0013" // ncDeviceOpen:1013 Failed to find booted device after boot
|| modelName == "face-detection-0105" // get_element_type() must be called on a node with exactly one output
|| modelName == "face-detection-0106" // get_element_type() must be called on a node with exactly one output
|| modelName == "person-vehicle-bike-detection-2004" // 2021.4+: ncDeviceOpen:1013 Failed to find booted device after boot
)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (targetId == DNN_TARGET_OPENCL && (false
|| modelName == "face-detection-0106" // Operation: 2278 of type ExperimentalDetectronPriorGridGenerator(op::v6) is not supported
)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (targetId == DNN_TARGET_OPENCL_FP16 && (false
|| modelName == "face-detection-0106" // Operation: 2278 of type ExperimentalDetectronPriorGridGenerator(op::v6) is not supported
)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if INF_ENGINE_VER_MAJOR_GE(2020020000)
if (targetId == DNN_TARGET_MYRIAD && backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (modelName == "person-detection-retail-0013") // IRv10
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
#if INF_ENGINE_VER_MAJOR_EQ(2020040000)
if (targetId == DNN_TARGET_MYRIAD && modelName == "person-detection-retail-0002") // IRv5, OpenVINO 2020.4 regression
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
ASSERT_EQ(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, backendId);
bool isFP16 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD);
const std::string modelPath = getOpenVINOModel(modelName, isFP16);
ASSERT_FALSE(modelPath.empty()) << modelName;
std::string xmlPath = findDataFile(modelPath + ".xml", false);
std::string binPath = findDataFile(modelPath + ".bin", false);
std::map<std::string, cv::Mat> inputsMap;
std::map<std::string, cv::Mat> ieOutputsMap, cvOutputsMap;
// Single Myriad device cannot be shared across multiple processes.
if (targetId == DNN_TARGET_MYRIAD)
resetMyriadDevice();
if (targetId == DNN_TARGET_HDDL)
releaseHDDLPlugin();
EXPECT_NO_THROW(runIE(targetId, xmlPath, binPath, inputsMap, ieOutputsMap)) << "runIE";
if (targetId == DNN_TARGET_MYRIAD)
resetMyriadDevice();
EXPECT_NO_THROW(runCV(backendId, targetId, xmlPath, binPath, inputsMap, cvOutputsMap)) << "runCV";
double eps = 0;
#if INF_ENGINE_VER_MAJOR_GE(2020010000)
if (targetId == DNN_TARGET_CPU && checkHardwareSupport(CV_CPU_AVX_512F))
eps = 1e-5;
#endif
#if INF_ENGINE_VER_MAJOR_GE(2021030000)
if (targetId == DNN_TARGET_CPU && modelName == "face-detection-0105")
eps = 2e-4;
#endif
#if INF_ENGINE_VER_MAJOR_GE(2021040000)
if (targetId == DNN_TARGET_CPU && modelName == "person-vehicle-bike-detection-2004")
eps = 1e-6;
#endif
EXPECT_EQ(ieOutputsMap.size(), cvOutputsMap.size());
for (auto& srcIt : ieOutputsMap)
{
auto dstIt = cvOutputsMap.find(srcIt.first);
CV_Assert(dstIt != cvOutputsMap.end());
double normInf = cvtest::norm(srcIt.second, dstIt->second, cv::NORM_INF);
EXPECT_LE(normInf, eps) << "output=" << srcIt.first;
}
}
INSTANTIATE_TEST_CASE_P(/**/,
DNNTestOpenVINO,
Combine(dnnBackendsAndTargetsIE(),
testing::ValuesIn(getOpenVINOTestModelsList())
)
);
typedef TestWithParam<Target> DNNTestHighLevelAPI;
TEST_P(DNNTestHighLevelAPI, predict)
{
initDLDTDataPath();
Target target = (dnn::Target)(int)GetParam();
bool isFP16 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD);
const std::string modelName = "age-gender-recognition-retail-0013";
const std::string modelPath = getOpenVINOModel(modelName, isFP16);
ASSERT_FALSE(modelPath.empty()) << modelName;
std::string xmlPath = findDataFile(modelPath + ".xml");
std::string binPath = findDataFile(modelPath + ".bin");
Model model(xmlPath, binPath);
Mat frame = imread(findDataFile("dnn/googlenet_1.png"));
std::vector<Mat> outs;
model.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
model.setPreferableTarget(target);
model.predict(frame, outs);
Net net = readNet(xmlPath, binPath);
Mat input = blobFromImage(frame, 1.0, Size(62, 62));
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableTarget(target);
std::vector<String> outNames = net.getUnconnectedOutLayersNames();
std::vector<Mat> refs;
net.forward(refs, outNames);
CV_Assert(refs.size() == outs.size());
for (int i = 0; i < refs.size(); ++i)
normAssert(outs[i], refs[i]);
}
INSTANTIATE_TEST_CASE_P(/**/,
DNNTestHighLevelAPI, testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE))
);
}}
#endif // HAVE_INF_ENGINE
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